Exploring policies for the reduction of child physical abuse and neglect☆

Joanne Klevensa, Sarah Beth L. Barnetta, Curtis Florencea, and DeWayne Mooreb

aCenters for Disease Control and Prevention, Division of Violence Prevention, Atlanta, GA, USA

bClemson College of Business and Behavioral Science, School of Psychology, Clemson, SC, USA

Abstract

Policies can be powerful tools for prevention given their potential to affect conditions that can

improve population-level health. Given the dearth of empirical research on policies' impacts on

child maltreatment, this article (a) identifies 37 state policies that might have impacts on the social

determinants of child maltreatment; (b) identifies available data sources documenting the

implementation of 31 policies; and (c) utilizes the available data to explore effects of 11 policies

(selected because they had little missing data) on child maltreatment rates. These include two

policies aimed at reducing poverty, two temporary assistance to needy families policies, two

policies aimed at increasing access to child care, three policies aimed at increasing access to high

quality pre-K, and three policies aimed at increasing access to health care. Multi-level regression

analyses between within-state trends of child maltreatment investigation rates and these 11

policies, controlling for states' childhood poverty, adults without a high school diploma,

unemployment, child burden, and race/ethnicity, identified two that were significantly associated

with decreased child maltreatment rates: lack of waitlists to access subsidized child care and

policies that facilitate continuity of child health care. These findings are correlational and are

limited by the quality and availability of the data. Future research might focus on a reduced

number of states that have good quality administrative data or population-based survey data on

child maltreatment or reasonable proxies for child maltreatment and where data on the actual

implementation of specific policies of interest can be documented.

Keywords

Child physical abuse; Child neglect; Policy evaluation

☆Partial data collection was conducted under contract with the Center for the Study of Social Policies with funding from the Doris Duke Charitable Foundation, in partnership with the CDC Foundation, and the Division of Violence Prevention of the Centers for Disease Control and Prevention. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Correspondence to: Joanne Klevens.

HHS Public AccessAuthor manuscriptChild Abuse Negl. Author manuscript; available in PMC 2016 February 01.

Published in final edited form as:Child Abuse Negl. 2015 February ; 40: 1–11. doi:10.1016/j.chiabu.2014.07.013.

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Introduction

Research on and use of public policies to prevent child maltreatment is surprisingly scant. A

recent review conducted to identify gaps in child maltreatment prevention found only three

studies examining public policies (Klevens & Whitaker, 2007). Two of these studies

examined the impact of welfare policies on child physical abuse and neglect (Fein & Lee,

2003; Paxson & Waldfogel, 2002, 2003), and one study looked at the impact of legislation

banning corporal punishment (Durrant, 1999). Other policies with prevention potential

examined in the literature not identified by Klevens and Whitaker (2007) include legislation

criminalizing fetal exposure to drugs (Chavkin, Wise, & Elman, 1998), child exposure to

partner violence being considered child neglect (Edleson, Gassman-Pines, & Hill, 2006),

increasing access to abortion (Bitler & Zavodny, 2004; Seiglie, 2004; Sen, Wingate, &

Kirby, 2012), and housing policies' effects on stability for children and availability of child

care (McAllister, Thomas, Wilson & Green, 2009).

This limited research on policies is problematic because policies can be powerful tools for

prevention given their potential to affect the conditions in which people are born, grow, live,

and work (i.e., social determinants) and improve population-level health (Commission on

Social Determinants of Health [CSDH], 2008). Even policies that are not formulated with

health in mind often have health consequences. For example, the Earned Income Tax Credit

has been associated with decreases in infant mortality (Arno, Sohler, Viola, & Schechter,

2009). Providing income support to families in poverty has been associated with decreases

in children's externalizing behaviors (Gennetian, Castells, & Morris, 2010), young adult

substance abuse (Costello, Erkanli, Copeland, & Angold, 2010), and delinquency (Akee,

Copeland, Keeler, Angold, & Costello, 2010).

Given the potential for prevention and dearth of empirical research on policies' impacts on

child maltreatment, we aimed to (a) identify state policies that might have impacts on the

social determinants of child maltreatment; (b) identify available data sources documenting

the implementation of these policies; and (c) utilize the available data to explore effects of a

selected set of policies on child maltreatment investigation rates. The first section of this

article describes our approach and findings for the first two aims. The second section

presents the methods and findings for the third aim. The final section will summarize the

findings, identify the limitations of our data, and suggest directions for future efforts. This

work may encourage other child maltreatment prevention researchers to go beyond

individual and family-level interventions and consider more research on policies' impacts.

Identification of Policies, Theoretical Links, and Data Sources

The term policy, as used in this study, includes any law, regulation, procedure,

administrative action, incentive, or voluntary practice of governments and other institutions

(Centers for Disease Control and Prevention, 2013). This study focuses on state-level

policies for two reasons. First, states and local governments contribute to two thirds of all

public spending on children (Isaacs, Hahn, Rennane, Steuerle, & Vericker, 2011), and

therefore, state-level public policies have substantial impacts on children. Second, the

variation across states and small but measurable change over time in the selection and

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implementation of policies offered multiple “natural experiments” which could facilitate the

evaluation of the impact of policies.

To identify state policies that might affect the social determinants of child maltreatment

rates we consulted with nine individuals identified by the Center for the Study of Social

Policy (CSSP) as policy experts in the area of child and family welfare, economics, public

health, health care, or environment and discussed with them how these policies might be

theoretically linked to child maltreatment. For the consultants, we defined social

determinants as the circumstances in which people are born, grow, live, work, and age

(CSDH, 2008). These consultants also provided suggestions for available data on state-level

policies. Internet searches and interviews with key informants identified by CSSP were used

to identify other available data on state-level policies. Table 1 presents the list of state

policies identified and, if found, the year(s) and source for which data were available on

their implementation.

The first group of policies identified addressed the issues of low income and poverty. Low

income has long been associated with maltreatment (Stith et al., 2009). The mechanisms

through which poverty or low income might increase child maltreatment include increased

parental stress as a result of perceived hardships (Gershoff, Aber, Raver, & Lennon, 2007;

Mistry, Vandewater, Huston, & McLoyd, 2002; Slack, Holl, McDaniel, Yoo, & Bolger,

2004; Yeung, Linver, & Brooks-Gunn, 2002), increased number of negative life events

(Gershoff et al., 2007), or because of poverty's effects on parents' mental health and

relationships with partners (Yeung et al., 2002).

Policies related to concentration of poverty were also proposed. Concentrated neighborhood

poverty is consistently associated with higher rates of child maltreatment (Coulton,

Crampton, Irwin, Spilsbury, & Korbin, 2007; Freisthler, Merritt, & LaScala, 2006).

Neighborhood disadvantage might increase child maltreatment through its effects on

parental depression (Mair, Diez Roux, & Galea, 2008), social capital (Zolotor & Runyan,

2006), willingness to rely on neighbors for child care (Garbarino & Sherman, 1980) or other

needs (Ernst, 2001), increased social disorder resulting in lack of social controls on

behaviors (Freisthler et al., 2006), or decreased access to resources and formal supports.

Housing policies were suggested because they affect access to affordable housing and

residential stability. Homelessness is a stronger predictor than parental substance abuse or

mental illness for out-of-home placements for children (Cowal, Shinn, Weitzman,

Stojanovic, & Labay, 2002). Whether a direct result of low income or its correlates,

homelessness or residential instability is in itself a stressor and it affects a parent's ability to

develop and maintain a local support network. In turn, weak or lack of social support is a

consistent risk factor for child maltreatment (Stith et al., 2009).

Policies related to access to high quality affordable childcare were proposed for two reasons.

First, childcare assistance can affect families' ability to work and earn sufficient income

(Schulman & Blank, 2004); as described previously, insufficient income may lead to child

maltreatment through various mechanisms. Second, enriched early experiences in high-

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quality care settings can reduce childhood and adolescent behavioral problems (Vandell et

al., 2010) which may trigger abusive parenting.

Evidence suggesting that pre-K programs involving parents may reduce child maltreatment

(Reynolds & Robertson, 2003) led to consideration of policies increasing access to high

quality pre-K. States vary considerably in the strategies pursued to increase the quality of

child care (e.g., allowing tiered reimbursement; and providing funding for quality

improvement grants, professional development systems building, care provider scholarships,

and/or wage enhancement initiatives) and pre-K programs (e.g., promoting comprehensive

early learning standards or parental involvement, establishing degree and training

requirements for teachers, setting maximum class size allowed and minimum staff–child

ratio, requiring that health and support services and meals be provided, and conducting site

visits).

Policies related to state provision of children's health care insurance (SCHIP) were proposed

because without insurance children are less likely to receive health services in a timely

manner (Institute of Medicine, 2002) which might lead to medical neglect. In addition to

considering increased thresholds for eligibility for lower income families, policy experts

proposed continuous eligibility and presumptive eligibility policies. Continuous eligibility

policies were considered because these enable states to ensure continuity of care by

providing Medicaid and SCHIP enrollees' continuous coverage for longer periods of time

rather than on a month-to-month basis. Presumptive eligibility policies enable states to

provide temporary coverage to children and pregnant women under Medicaid and SCHIP

until a formal eligibility determination can be made.

Policies related to parents' health insurance and access to mental health care, substance

abuse treatment, and contraception were also suggested. Such policies were included

because anxiety, depression, psychopathology, substance abuse, and unplanned pregnancies

are risk factors for child maltreatment (Stith et al., 2009).

Exploration of Potential Effects of Policies on Child Maltreatment Investigation Rates

We examined the potential statistical effects of the 11 policies that had the most complete

set of state-level yearly data for 2000 to 2009. The 11 policies selected for these analyses are

numbered in the first column of Table 1 along with a description and the sources of data

used to document these policies.

Methods

Variables

The dependent variable for these policy analyses, state-level child maltreatment

investigation rate, was based on the U.S. Department of Health and Human Services'

(DHHS) Administration for Children and Families (2002, 2003, 2004, 2005, 2006, 2007,

2008, 2009, 2010a, 2010b) annual reports on child abuse and neglect. These reports contain

data on the number of referrals (or allegation of maltreatment) to Child Protective Services

(CPS), the number of cases screened in (i.e., investigated), and the number of cases

determined to be maltreated or substantiated by CPS for the 50 states and the District of

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Columbia. We chose to utilize cases screened in (per 1,000 children in each state) as the

outcome for various reasons. A screened in referral means that an allegation of child abuse

or neglect met the State's standard for investigation. The rate of investigations in the United

States has remained constant for at least the past five years (DHHS, 2010), which suggests

some level of consistency in this determination despite changes in response (e.g., the

development of differential response models). Screened in cases include both substantiated

and unsubstantiated cases. Although including unsubstantiated cases in our rates might seem

counterintuitive, there is growing recognition in the field that this inclusion provides a better

indicator of child maltreatment given that multiple studies find little or no difference

between substantiated and unsubstantiated cases in regards to risk factors or future risk

(Drake, Jonson-Reid, Way, & Chung, 2003; English, Marshall, Brummel, & Orme, 1999;

Hussey et al., 2005; Jonson-Reid, Drake, Kim, Porterfield, & Han, 2004; Kohl, Jonson-Reid,

& Drake, 2009). It also avoids the problem of substantiation rates changing as a result of

new procedures or response decisions implemented by child protective services (e.g.,

alternative response tracks). We refer to this dependent variable as child maltreatment

investigations throughout the manuscript.

The independent variables were the 11 policies identified as havinig the most complete data

set. These variables included:

(a) state minimum wage (U.S. Department of Labor, 2013),

(b) the percentage of income owed in state income taxes (tax burden; calculated

with version 9.0 of TAXSIM; Feenberg & Coutts, 1993; National Bureau of

Economic Research, 2012) for families at the federal poverty line (U.S.

Department of Health and Human Services, 2011a,b),

(c) the maximum earnings applicants could receive and still be eligible for TANF

benefits in each state (maximum monthly earnings and still eligible) (Rowe,

McManus, & Roberts, 2004; Rowe & Murphy, 2006, 2009; Rowe, Murphy, &

Kaminski, 2008; Rowe, Murphy, & Williamson, 2006a, 2006b; Rowe, Murphy,

& Mon, 2010; Rowe & Roberts, 2004; Rowe & Russell, 2004; Rowe &

Versteeg, 2005),

(d) monthly benefits for a family of three, both adjusted for 2009 dollars (Rowe et

al., 2004; Rowe & Murphy, 2006, 2009; Rowe et al., 2008; Rowe et al.,

2006a,b; Rowe et al., 2010; Rowe & Roberts, 2004; Rowe & Russell, 2004;

Rowe & Versteeg, 2005),

(e) the income cutoff for child care subsidies for a family of three in each state

(Blank, 2001; National Women's Law Center, 2004, 2006, 2007, 2008, 2009),

(f) existence of a waitlist for child care in each state (Blank, 2001; National

Women's Law Center, 2004, 2006, 2007, 2008, 2009),

(g) percent above the federal poverty line at which children one to five years old

were eligible for Medicaid/SCHIP (eligibility for Medicaid/SCHIP; Kaiser

Commission on Medicaid and the Uninsured, 2000, 2002, 2003, 2004, 2005,

2007, 2008, 2009a,b),

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(h) continuity of eligibility for Medicaid/SCHIP (Kaiser Commission on Medicaid

and the Uninsured, 2000, 2002, 2003, 2004, 2005, 2007, 2008, 2009a,b),

(i) presumptive eligibility for Medicaid/SCHIP (Kaiser Commission on Medicaid

and the Uninsured, 2000, 2002, 2003, 2004, 2005, 2007, 2008, 2009a,b),

(j) percent of 3- and 4-year olds in pre-K (National Institute for Early Education

Research, 2003, 2004, 2005, 2006, 2007, 2008, 2009), and

(k) expenditures per child in pre-K in each state (available for 2001–2009; National

Institute for Early Education Research, 2003, 2004, 2005, 2006, 2007, 2008,

2009).

Thus, the data consisted of 510 potential observations (where an observation is a state/year

pair), representing 50 states and the District of Columbia during 10 years. There were 63

instances of missing state child maltreatment investigation rates (12%). Missing data for the

policy analyses ranged from 157 observations (31%) for state pre-K expenditures to 42

observations (8%) for state minimum wage. All analyses treated missing data as missing.

The following variables were considered potential confounders: state percentages of

childhood poverty (U.S. Census Bureau, 2011c), population over 18 years of age with high

school diploma (U.S. Census Bureau, 2011a), proportion of Black and Latino population

(U.S. Census Bureau, 2011b), annual average unemployment rate (U.S. Department of

Labor, 2012), and the state ratio of children 0–17 to adults 18–64 (U.S. Census Bureau,

2012).

Analyses

To establish potential effects of policies on states' rates, multi-level regression model

analyses was used to estimate the within state trends in child maltreatment investigation

rates as a function of a specific policy controlling for all confounders mentioned previously.

In addition, we also estimated models with up to three policies of similar nature to adjust for

these simultaneously (e.g., TANF eligibility and TANF benefits were in the same model).

For descriptive purposes, there was evidence of substantial clustering of measurement

occasions (Level 1) within states (Level 2). For all variables, the proportion of state level

variance was substantial (assessed by treating each predictor as a dependent variable in an

intercept only model to establish intraclass correlation coefficients (ICCs)). All ICCs were

over .50, except for the measure of child care quality (ICC = .14), indicating over half of the

variation in the assessments occurred across states. Multi-level models were selected to

adjust for the clustering of time within states.

We focus on the results for the Level 1 predictors. All Level 1 predictor variables were

centered around the state mean (Hoffman & Gavin, 1998). Values that deviated by more

than 4.5 standard deviations from the expected value were excluded from the analyses to

reduce error and avoid excess influence on effects. The time trend in child maltreatment

investigation rates was included as a fixed effect. The policy variables were measured as

differences from the state mean for the study period in order to control for unobservable

state differences that are constant during the study period. A state-level random effect

(random intercept) was also included to model unexplained variation across states in child

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maltreatment investigation rates. Because trends over time were likely to vary by state we

also model a random slope for time. Fit statistics based on the reduction in the −2 Restricted

Log Likelihood (REML was used instead of ML estimation) and Schwarz's Bayesian

Criterion (BIC) indicated improvement in fit by modeling a random slope using the

unstructured option for the covariance structure.

Given the large number of policies examined and the potential for collinearity among the

policy variables, all 11 policy variables were not entered into the model simultaneously.

Instead, separate models were estimated with each policy individually. As a sensitivity

analysis, models were also estimated that included all policies in a given policy category,

such as all state Medicaid policies.

Results of Policy Analyses

Between 2000 and 2009, the rate of child maltreatment investigations ranged from 7.77 to

97.2 per 100,000 children (M = 26.7, SD = 10.3). Overall, these rates tended to decrease

over the time period.

Table 2 presents descriptive statistics for the selected policies, standardized β regression

coefficients and marginal effects (for each policy alone and, in parentheses, adjusted for

similar policies) derived from the multi-level regression models controlling for demographic

confounders. Marginal effects are calculated as the change in the dependent variable

associated with a one-unit change in the policy variable. Standard errors for marginal effects

are presented in Table 3. The absence/presence of wait lists to access child care had a

statistically significantly association with child maltreatment investigation rates. More

specifically, the presence of wait lists to access subsidized child care was associated with an

increase in maltreatment investigations of 3.13 per 1,000 children.

States' continuity of eligibility for Medicaid/SCHIP was statistically significant and

associated with lower child maltreatment investigation rates. States with continuous

eligibility for Medicaid/SCHIP have child maltreatment investigation rates that are 2.55 per

1,000 lower than states without continuous eligibility.

Discussion

In this article, 37 different policies are identified that might have impacts on the social

determinants of child maltreatment. For 31 of these policies, there is at least one data point

documenting their existence at the state-level. Multiple years of data are readily available for

some unevaluated policies such as the Earned Income Tax Credit, work requirements for

single parents with young children, and those aimed at increasing availability of affordable

housing. This list is not exhaustive but suggests several opportunities for child maltreatment

prevention researchers and others interested in policy evaluation. It may also be of interest to

those concerned with child well-being in general.

We also examined the statistical relationship between child maltreatment investigation rates

and four policies targeting poverty reduction, two policies facilitating access to child care,

two policies facilitating access to early childhood education (pre-K), and three policies

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facilitating children's access to health care. We found a statistically significant negative

effect of waitlists to access subsidized child care and a statistically significant positive effect

for policies that facilitate continuity of child health care insurance which are consistent with

the expectation that greater access to child care and continuous access to health care could

potentially decrease child maltreatment rates.

Our models control for a wide array of demographic confounders, the presence of similar

policies, and the policy variables are measured as deviations from the state mean to control

for time-invariant state-level unobservables and common trends in reports of child

maltreatment. We report only within state trends to avoid issues with the variation in state

definitions of what constitutes child maltreatment and who is mandated to report. However,

there may be other confounders we have not controlled for that may be time-variant within

states and potentially correlated with these state policies. On the other hand, given that

policies might be more effective if implemented as part of a larger package, controlling for

similar policies occurring simultaneously may be considered excessive.

Our statistical analyses have other limitations that warrant cautious interpretation for both

the significant findings as well as the nonsignificant findings. Although the data reported by

the Administration for Children and Families are extremely useful, there are many issues

related to using child maltreatment reports to CPS (Fallon et al., 2010). Administrative

protocols, regulations, and definitions vary across states. In addition, we chose to focus only

on allegations of child abuse or neglect that met the State's standard for investigation (i.e.,

screened in). However, because we only report within state trends, these issues would be of

concern if they vary within states over time.

Our use of official reports is also a potential limitation. Reports to CPS underestimate the

occurrence of child maltreatment (Theodore et al., 2005), and future research should

examine policies' impacts on self-reported child maltreatment.

These statistical analyses are also limited by the indicators or data used to measure the

implementation of a policy. We have no information on how well the policies were

implemented, what proportion of the population targeted was covered or actually complied,

or whether the policies affected those at highest risk. We also lack information on the

implementation of competing policies or contextual factors that may have influenced

implementation or child maltreatment investigation rates. In addition, we have no

information on whether these policies effectively achieved their purpose, and if they did,

whether there was a time lag between the implementation of the policy and this

achievement. Future research should try to collect this type of information in order to be

able to know whether the absence of effects is because of poor implementation, reduced

coverage, inequitable coverage, a time lag, competing policies or contextual factors, or

ineffective policies.

Another limitation to consider is that the associations identified occur at the state level and

not at the family or child level. Thus, although we report a significant association between

increased state rates of child maltreatment investigations and the presence of child care

waiting lists, we cannot conclude that a child being on a child care waiting list is associated

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with that child's increased risk for being investigated for child maltreatment. However, state-

level time trend analyses can suggest a potential relationship between policy changes and

maltreatment reports that deserves further exploration. Future research using different

research designs is needed to understand the pathways by which policy changes may be

linked to child maltreatment.

Finally, it is important to acknowledge that our study design does not permit us to draw

conclusions about causality from the analyses. Although the longitudinal design and our

ability to adjust for confounders in our analyses add strength to our conclusions, these

strategies do not overcome key potential biases inherent to quasi-experimental studies. For

this reason, our findings should be viewed as correlational.

Although the randomized controlled trial is the most robust form of evidence of

effectiveness and is increasingly used to evaluate policies (Ludwig, Kling, & Mullainathan,

2011), in many instances, the costs, time constraints, complexity of the policy, or ethical

concerns makes randomized designs impractical to evaluate public policies. In these cases,

natural experiments offer good opportunities for examining policies' impacts. Future

research might focus on a reduced number of states that have good quality administrative

data or population-based survey data on child maltreatment or reasonable proxies for child

maltreatment and where data on the actual implementation of specific policies of interest

could be documented. Combining qualitative methods with quantitative might contribute to

a better understanding of how a policy is implemented and in what context. Such data could

go a long way towards addressing the issues raised above and would contribute to a better

evidence base to inform policies that might affect child maltreatment.

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Table 1

Policies identified, description, data source, and years data available.

Policies Description Data source Years of data available

Reduce poverty

1. Minimum wage Percent above federal minimum income

US Department of Labor 1968–annual

2. Income taxes on low-income households

Percentage of income owed in state income taxes for a family at the federal poverty level

National Bureau of Economic Research (2012)Department of Health and Human Services (poverty threshold)

1977–annual

Threshold for state taxes Income threshold (% above federal poverty line) at which family income becomes subject to the state income tax

Center on Budget and Policy Priorities

1996–annual

Earned Income Tax Credit Tax refund for low to moderate income working individuals and families

Tax credits for working families

Year enacted by state

Child Tax Credit Tax credit per child under 17 years of age

Tax credits for working families

Current

3. Eligibility for TANF Maximum earnings an applicant can receive and still be eligible for benefit

Urban Institute 2000–annual

4. TANF benefits Maximum monthly benefit for a family of three with no income in the month of July

Urban Institute 2000–annual

Work requirement exemptions Exemptions for single parents with young children

Urban Institute 2000–annual

Child support rules Child support collected passed on or not to families receiving TANF assistance/disregard the passed through support income in determining eligibility for and the amount of assistance

Vinson and Turetsky (2009)

2008

Reduce predatory lending Prohibit or cap fees and interest rates for credit cards, payday lenders, pawnshops, title lenders, subprime mortgage lenders, rent-to-own stores

AARP (n.d.)National Conference of State Legislatures (2008, 2011, 2012)

20002007–2012

Increase access to and use of traditional banking services for unbanked populations

No data source found No data

Deconcentrate poverty Use of the Low Income Housing Tax Credit (LIHTC) to locate affordable housing in low poverty neighborhoods

US Department of Housing and Urban Development

1996–2009

Increase stability of residence

Affordable housing Existence and size of trust fund to build or preserve affordable homes

National Council of State Housing Agencies

1993–annual

Targeting federal tax credits to increase the supply of affordable homes for families with restricted access to private housing markets

National Council of State Housing Agencies

1993–annual

Statutes prohibiting discrimination against housing voucher holders

National Housing Law Project

2005, 2010

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Policies Description Data source Years of data available

Subsidized through vouchers or provision of public housing

No source found with state by state data

No data

Statutes preventing mortgage fraud

National Conference of State Legislatures

2000–2011

Provide high quality, affordable child care

5. Access to subsidies Income cutoff for a family of three for child care subsidy as a percentage of state median income

National Women's Law Center

2001–annual

6. Wait list Presence/absence of a wait list for child care assistance

National Women's Law Center

2001–annual

Reimbursement rates Meets/does not meet federal guideline of the 75th percentile of a recent market rate survey

National Women's Law Center

2001–annual

Co-pays % of family income expected to pay for child care

USDHHS/ACF 1999–2000, 2002–2003, 2005–2006

Quality of care Funding provided for tiered reimbursement, quality improvement grants, professional development systems building, care provider scholarships, and/or wage enhancement initiatives

National Women's Law Center

2001–annual

Access to friends, family, or neighbor care

Non-licensed Family Child Care and/or In-Home providers allowed to receive subsidy payments

USDHHS/ACF 2001, FY02–03; FY04–05; FY06–07; FY10–11

Quality of friends, family or neighbor care

Requirements for background checks, health and safety attestation and/or orientation/training required; training, materials and equipment, or career development opportunities offered

Porter and Kearns (2005) 2004

Family and medical leave Paid maternity leave and leave to care for sick children

National Partnerships for Women and Families (2005)

2004, 2006

Provide high quality, affordable Pre-K

Increase access Fund directly or supplement funds to Head Start

USDHHS/ACF 2003, 2005

7. Coverage of pre-K Percent of three and four year olds enrolled in state funded pre-K

National Institute for Early Education Research

2001–annual

8. Quality of pre-K Expenditure in dollars per child adjusted for inflation

National Institute for Early Education Research

2001–annual

Promote comprehensive early learning standards, degree and training requirements for teachers; maximum class size allowed; minimum staff–child ratio; health and support services provided; meals provided; site visits conducted; parental involvement

State plans available at USDHHS/ACFPolicy enactment data not available

Facilitate children's access to health care

9. Eligibility Maximum family income as a percent of the federal poverty line at which children 1–5 are eligible for Medicaid or the State Children's Health Insurance Program (S-CHIP)

Kaiser Commission on Medicaid and the Uninsured

2001–annual

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Policies Description Data source Years of data available

10. Continuity of eligibility Presence/absence of continuous eligibility enables states to ensure continuity of care by providing Medicaid and SCHIP enrollees continuous coverage for, most commonly, 12 months rather than on a month-to-month basis for Medicaid for children/SCHIP

Kaiser Commission on Medicaid and the Uninsured

2000–annual

11. Presumptive eligibility Presence/absence of presumptive eligibility to provide temporary coverage to children and pregnant women under Medicaid and SCHIP until a formal eligibility determination can be made

Kaiser Commission on Medicaid and the Uninsured

2000–annual

Medical home for every child Accessible, family-centered, continuous, comprehensive, coordinated, compassionate, and culturally effective medical care

State-wide effort not identified

No data

Evidence-based services Evidence-based care and services for children with disabilities as defined by the Individual with Disabilities Education Act

DOE, Office of Special Education (only coverage)

1996–annual

Facilitate parent's access to health care

Eligibility for Medicaid Maximum earnings an applicant can receive and still be eligible for benefit

Kaiser Commission on Medicaid and the Uninsured

2001–annual

Access to contraception Required coverage from private health insurance providers or coverage through Medicaid waivers, or increased funding of community-based health clinics

Guttmacher Institute 2001–2002, 2006, 2008

Access to mental health & substance abuse treatment

Provision of outpatient, residential, crisis and/or services for family

Robinson, Kaye, Bergman, Moreaux, and Baxter (2005)

2003

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Table 2

Range and mean scores (SD), adjusted standardized regression coefficients and marginal effects of each policy

alone (and adjusted for similar policies) on child maltreatment investigations.

Policies (unit of measurement) n Range M (SD) Standardized β policy alone/adjusted for similar

policiesa

Marginal effects of policy alone/adjusted

for similar policiesa

Reducing poverty

Minimum wage (% above federal minimum wage)

468 −17 to 54 8.42 (13.67) .02/.01 0.625/0.536

State tax burden ($) for family of 3 at federal poverty line

443 −0.10 to 0.03 −0.005 (0.02) −.05/−.05 −18.111/−17.107

TANF policies

Maximum monthly earnings and still be eligible (US2009$)

459 0.0–1696.86 820.54 (340.31) −.04/−.03 −0.014/−0.014

Maximum monthly benefits for family of 3 (US 2009$)

459 169.40–1149.93 468.41 (176.91) −.04/−.03 −0.002/−0.0001

Access to child care

Income cutoff for child care subsidies for family of 3 (% of state median income)

408 33.82–95.30 57.76 (12.62) −.01/−.01 0.009/−0.030

Wait list for child care (no/yes) 404 0–1 .38 (.49) .07/.07** 3.127**/2.238*

Access to quality pre-K

Enrollment in pre-K for 4-year olds (%) 354 0–71 12.71 (15.97) .02/.01 −0.017/−0.022

Enrollment in pre-K for 3-year olds (%) 354 0–27 2.43 (4.51) .02/.02 0.056/0.087

Expenditure per child in pre-K (US 2009$)

353 0–11,797 3417.54 (2812.41) .02/.01 0.0002/0.0002

Access to health care

Eligibility for Medicaid/SCHIP for children 1–5 years old (% of federal poverty limit)

457 133–300 162.65 (44.10) .01/.03 0.012/0.012

Continuity of eligibility for Medicaid/SCHIP (no/yes)

408 0–1 .32 (.47) −.047*/−.03 −2.551*/−2.504*

Presumptive eligibility for Medicaid/SCHIP (no/yes)

408 0–1 .16 (.37) −.03/−.03 −0.435/−0.271

Notes: Regression coefficients adjusted for state level percent of childhood poverty, high-school graduation among population > 18, unemployment, Black, and Latino population, and the child dependency ratio. Marginal effects represent the change in the child maltreatment investigation rate associated with a one unit change in the independent variable.

aAdjusted for policies in same category (e.g., eligibility, continuity, and presumptive eligibility for Medicaid/SCHIP included in one model).

*p < .05.

**p < .01.

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Table 3

Adjusted standardized regression coefficients and marginal effects and standard errors of each policy alone

(and adjusted for similar policies) on child maltreatment investigations.

Policies (unit of measurement) Standardized β policy alone/adjusted for

similar policiesa

Marginal effects of policy alone/adjusted for similar policies Standard errors in parentheses

Reducing poverty

Minimum wage (% above federal minimum wage) .02/.01 0.625(3.07)/0.536(3.08)

State tax burden ($) for family of 3 at federal poverty line −.05/−.05 −18.111(35.66)/−17.107(35.73)

TANF policies

Maximum monthly earnings and still be eligible (US2009$) −.04/−.03 −0.014(0.01)/−0.014(0.01)

Maximum monthly benefits for family of 3 (US2009$) −.04/−.03 −0.002(0.004)/−0.0001(0.004)

Access to child care

Income cutoff for child care subsidies for family of 3 (% of state median income)

−.01/−.01 0.009(0.05)/−0.030(0.04)

Wait list for child care (no/yes) .07/.07** 3.127**(1.10)/2.238*(1.02)

Access to quality pre-K

Enrollment in pre-K for 4-year olds (%) .02/.01 −0.017(0.03)/−0.022(0.03)

Enrollment in pre-K for 3-year olds (%) .02/.02 0.056(0.21)/0.087(0.21)

Expenditure per child in pre-K (US2009$) .02/.01 0.0002(0.0003)/0.0002(0.0003)

Access to health care

Eligibility for Medicaid/SCHIP for children 1–5 years old (% of federal poverty limit)

.01/.03 0.012(0.01)/0.012(0.01)

Continuity of eligibility for Medicaid/SCHIP (no/yes) −.04*/−.03 −2.551*(1.13)/−2.504*(1.14)

Presumptive eligibility for Medicaid/SCHIP (no/yes) −.03/−.03 −0.435(1.05)/−0.271(1.05)

Notes: Regression coefficients adjusted for state level percent of childhood poverty, high-school graduation among population > 18, unemployment, Black, and Latino population, and the child dependency ratio. Marginal effects represent the change in the child maltreatment investigation rate associated with a one unit change in the independent variable.

aAdjusted for policies in same category (e.g., eligibility, continuity, and presumptive eligibility for Medicaid/SCHIP included in one model).

*p < .05.

**p < .01.

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